inclusive razor analysis results – 27-10-11crogan/files/razor_27_10_11.pdf · inclusive razor...
TRANSCRIPT
+
Inclusive Razor Analysis Results – 27-10-11
A. Apresyan, Y. Chen, Christopher Rogan, J. Duarte, E. Di Marco, J. Lykken, A. Mott, M. Pierini, W. Reece, E. Salvati, M. Spiropulu
[GeV]RM100 150 200 250 300 350 400
/ 2
GeV
evt
N
1
10
210
310
410R > 0.15R > 0.20R > 0.25R > 0.30R > 0.35R > 0.40R > 0.45R > 0.50
=7 TeVsCMS 2010 Preliminary
-1 L dt = 35 pb∫ [GeV] (R* > 0.2)R*M
R*γ
200 400 600 800 1000 1200 1400
/ 40
GeV
evt
N
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510 tAll tTCHELTCHEMTCHETTCHEL+TCHELTCHEM+TCHELTCHET+TCHEL
=7 TeVsCMS 2011 Simulation
[GeV] (R* > 0.2)R*MR*γ
200 400 600 800 1000 1200 1400
)A
LL /
Ni C
ATR
(N 00.20.40.60.8
1
+ Analysis Status 2
n 800 pb-1 analysis has been green-lighted for approval (Nov. 9)
n CADI: http://cms.cern.ch/iCMS/jsp/analysis/admin/analysismanagement.jsp?ancode=SUS-11-008
n Twiki: https://twiki.cern.ch/twiki/bin/view/CMS/SUS11008
+ The 2011 Razor Analysis
n Based on variables MR and R (as last year)
n Use six exclusive boxes (three last year) defined according to lepton multiplicity
n In each box, define several exclusive signal regions (rather than one per box)
n Obtain a bkg prediction through a ML fit in the low R/MR sideband using a 2D parameterization (last year 1D)
n Combine several boxes/signal regions in one inclusive hypothesis test (for limit on the cross section or for accessing discovery)
3
+ 4 Search with MR + R (Razor) Introduced “Razor” variables, R and MR, designed to discover and characterize massive pair-production
Scale:
= �MMET
MRT =
�| �M |(|�p|+ |�q|)− �M · (�p+ �q)
2
q̃q̃ → (qχ̃01)(qχ̃
01)Example:
M∆ =m2
q̃ −m2χ̃01
2mq̃
M∆ =m2
q̃ −m2χ̃01
2mq̃
Peaks at
Edge at
Angle: R =MR
T
MR
MR =�
(|�p|+ |�q|)2 − (pz + qz)2
arXiv:1006.2727
�p �qArranging all reconstructed objects into two hemispheres, with 3-momenta and
+ 5 Search with MR + R (Razor) Introduced “Razor” variables, R and MR, designed to discover and characterize massive pair-production
Scale:
= �MMET
MRT =
�| �M |(|�p|+ |�q|)− �M · (�p+ �q)
2
Angle: R =MR
T
MR
MR =�
(|�p|+ |�q|)2 − (pz + qz)2
arXiv:1006.2727
�p �qArranging all reconstructed objects into two hemispheres, with 3-momenta and
tt̄+ jetsW + jets
+ Razor Triggers 6 See previous trigger talks:
u In addition to fully hadronic triggers, there are R/MR x-triggers with: Æ Single Muon Æ Single Electron Æ B-tagged jet Æ Single Photon Æ Double Photon
u Deployed online after May technical stop
https://indico.cern.ch/getFile.py/access?contribId=6&resId=0&materialId=slides&confId=141034 https://indico.cern.ch/getFile.py/access?contribId=6&resId=0&materialId=slides&confId=135391
n Suite of Razor triggers designed to capture events in most interesting region of R/MR plane
Used in the analysis presented See back-up slides for more details
+ Datasets
n Boxes indicate PD’s w/ Razor triggers
7
+ Baseline Event selection 8
…
L2L3 Corrected Calo Jets w/ FastJet PU subtraction
L2L3 Corrected PF Jets
L2L3 Corrected PF Jets w/ FastJet PU subtraction
Uncorrected track Jets
pT > 40 GeV/c
pT > 15 GeV/c
|η| < 3.0
|η| < 2.4pT > 40 GeV/cpT > 40 GeV/c
|η| < 3.0 |η| < 3.0
Loose Jet ID Loose Jet ID Loose Jet ID Consistent
w/ PV
and more
n Standard HCAL DPG HBHE Noise Filter + other event filters (see back-up)
n Jet ID and selection
n Require at least 2 jets with pT > 60 GeV/c (requirement from L1 trigger seed) - All jets (of a given type) clustered into two mega-jets
n two mega-jets and PF MET are used to calculate variables R and MR
+ Baseline Event selection 9
L2L3 Corrected Calo Jets w/ FastJet PU subtraction
pT > 40 GeV/c
|η| < 3.0
Loose Jet ID
n Standard HCAL DPG HBHE Noise Filter + other event filters (see back-up)
n Jet ID and selection
n Require at least 2 jets with pT > 60 GeV/c (requirement from L1 trigger seed) - All jets (of a given type) clustered into two mega-jets
n two mega-jets and PF MET are used to calculate variables R and MR
Default used in this analysis Chosen for consistency with online trigger objects
+ Offline Lepton Selection 10
Electrons Muons
See: https://twiki.cern.ch/twiki/bin/view/CMS/SimpleCutBasedEleID2011
Cut based selection a la VBTF 2010
We use ‘WP80’ and ‘WP95’
Cut based selection identical to VBTF 2010, with ‘Tight’ and ‘Loose’ working points except for isolation
(see below)
When isolation requirements are applied (WP80,95 and Tight muon) the combined (ECAL+HCAL+tracker) isolation is used, and is corrected for
PU dependence using the FastJet-derived energy density ρ. The PU-corrected combined isolation for isolation cone size R, ISOR
CORR, can be expressed in terms of the non-corrected quantity, ISOR
UNCORR, as:
ISOCORR
R = ISOUNCORR
R − πR2ρ
+MR shape dependence on Lep ID 11
[GeV] (R* > 0.2)R*MR*γ
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Loose ID
Tight ID
=7 TeVsCMS 2011 Simulation
[GeV] (R* > 0.2)R*MR*γ
100 200 300 400 500 600 700 800 900 1000
)A
LL /
Ni C
ATR
(N 0.80.850.9
0.951
W+jets Madgraph MC CALO JETS
n We want to understand the potential effect of our muon selection on the distribution of MR
n Define ‘baseline/denominator’ as all simulated events with a generator level muon within acceptance (pT and η) [ ]
n Apply lepton ID on top of baseline to see effect on MR distribution
n Only small dependence observed
All W (µν)
MR [GeV] (R > 0.2)
MR [GeV] (R > 0.2)
+ 12
[GeV] (R* > 0.2)R*MR*γ
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510 )νAll W(e
WP 95
WP 80
=7 TeVsCMS 2011 Simulation
[GeV] (R* > 0.2)R*MR*γ
100 200 300 400 500 600 700 800 900 1000
)A
LL /
Ni C
ATR
(N
0.60.70.80.9
1
W+jets Madgraph MC CALO JETS
MR shape dependence on Lep ID
n We want to understand the potential effect of our electron selection on the distribution of MR
n Define ‘baseline/denominator’ as all simulated events with a generator level electron within acceptance (pT and η) [ ]
n Apply lepton ID on top of baseline to see effect on MR distribution
n Only small dependence observed (stronger in ‘turn-on’ low MR region)
All W (eν)
MR [GeV] (R > 0.2)
MR [GeV] (R > 0.2)
+ Final State Boxes 13
MU Box
o ‘Tight’ muon
HAD Box
o Veto on lepton boxes
u Disjoint boxes based on physics object ID allows us to isolate different physics processes
u Lepton boxes, along with a QCD control sample, are used for the background prediction in the hadronic signal box (along with predictions in lepton boxes’ signal regions)
u Possibilities for sub-divisions within ‘boxes’ (isolation inversion for QCD, b-tag categories, lepton charge(s), etc.)
pµT > 10 GeV/c
MUMU Box
o ‘Tight’ muon+ ‘Loose’ muon
EMU Box
o ‘Tight’ muon+ WP80 electron
EE Box
o WP80 electron+ WP95 electron
pµT > 10 GeV/c
ELE Box
o WP80 electron
peT > 20 GeV/c peT > 20/10 GeV/cpµT > 15/10 GeV/c
peT > 20 GeV/c
+ 14
MU Box
HAD Box
MUMU Box
EMU Box
ELE Box
Final State Boxes (Tight MU pT > 10 && WP80 ELE pT > 20)?
(Tight/Tight MU pT > 15/10)?
(WP80/WP95 ELE pT > 20/10)?
EE Box
(Tight MU pT > 10)?
(WP80 ELE pT > 20)?
NO
NO
NO
NO
NO
YES
YES
YES
YES
YES
Box selection hierarchy ensures orthogonality of different box datasets
+ ML Fit Strategy
15
MU Box
o ‘Tight’ muon
HAD Box
o Veto on lepton boxes
ELE Box
o WP80 electron pµT > 10 GeV/c
MUMU Box
o ‘Tight’ muon+ ‘Loose’ muon EMU Box
o ‘Tight’ muon+ WP80 electron
EE Box
o WP80 electron+ WP95 electron
Z+jets top+X
W+jets
Put it all together…
pµT > 10 GeV/c peT > 20 GeV/c
peT > 20/10 GeV/cpµT > 15/10 GeV/c
peT > 20 GeV/c
15
+1D MR Views
n Pre-scale, low-threshold jet triggers allow us to probe kinematic region dominated by QCD mult-jets
n MR shape well-modeled by single exponential (in this region)
n Slope of this exponential has linear dependence on value of (R cut)2
16 Jet PD + (HLT_DiJetAve30 || HLT_Jet30)
f(MR) ∝ e−SMR
S = a+ b(R cut)2
+1D R2 Views
n Pre-scale, low-threshold jet triggers allow us to probe kinematic region dominated by QCD mult-jets
n R2 shape well-modeled by single exponential (in this region)
n Slope of this exponential has linear dependence on value of (R cut)2
17 Jet PD + (HLT_DiJetAve30 || HLT_Jet30)
S = c+ d(MR cut)
f(R2) ∝ e−SR2
+ From 1D To 2D n 1-D projections of MR(R2) and slope dependence on R2(MR) cut implies
specific 2-D functional form:
n Most general function which recovers the MR (R2) exponential behavior when integrated over R2 (MR)
With:
f(R2,MR) ∝�k(MR −M0
R)(R2 −R2
0)− 1�e−k(MR−M0
R)(R2−R20)
b (from MR view) = d (from R2 view) = k (from 2D view)
18
Confirmed in MC and data
+ MR shape dep. on B-tag 19
[GeV] (R* > 0.2)R*MR*γ
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510 tAll tTCHELTCHEMTCHETTCHEL+TCHELTCHEM+TCHELTCHET+TCHEL
=7 TeVsCMS 2011 Simulation
[GeV] (R* > 0.2)R*MR*γ
200 400 600 800 1000 1200 1400
)A
LL /
Ni C
ATR
(N 00.20.40.60.8
1
tt+jets Madgraph MC CALO JETS
n We want to understand the potential effect of b-tagging on the distribution of MR
n We consider the ‘Track Counting High Efficiency’ (TCHE) tagger with the loose (L), medium (M) and tight (T) working points
n The inclusive ttbar MR distribution (all final state boxes) is compared against single and double b-tag su-samples’
n Strong dependence observed in low MR ‘turn-on’ region – small shape dependence in exponentially falling region of MR
MR [GeV] (R > 0.2)
MR [GeV] (R > 0.2)
+ 1D MR view (W+jets) 20
PD SingleMu May10 ReReco + HLT_IsoMu17+TCHEM b-tag veto
n Functionally, same is true for W+jets (in fact, holds for all background considered)
n Modeled with 2 distinct components (as last year)
n Each component has unique functional parameters
+ 1D MR view (W+jets) 21
PD SingleMu May10 ReReco + HLT_IsoMu17+TCHEM b-tag veto
W+jets Madgraph simulation, same offline selection as above
Excellent q
ualitative and q
uantitative d
ata / MC
agreem
ent
+ 1D R2 view (W+jets) 22
PD SingleMu May10 ReReco + HLT_IsoMu17+TCHEM b-tag veto
W+jets Madgraph simulation, same offline selection as above
Excellent q
ualitative and q
uantitative d
ata / MC
agreem
ent
+ From 1D To 2D n 1-D projections of MR(R2) and slope dependence on R2(MR) cut implies
specific 2-D functional form (unique parameters for each ‘component’):
n Each relevant background (W+jets, Z+jets, ttbar+jets, …) is described with two unique 2D components:
23
+ Multi-Box Fit Introduction
n Each box contains several contributions n Wln, Zll, TTj, Znunu: Main SM backgrounds
n Each may have one or two components n Second components may be in common (e.g. ISR)
n The characteristic mass scales are different n MW, MZ, MTT
n Different shapes in R2/MR plain (turn on/tails)
n MR and R2 are correlated n Use 2D PDF to take advantage of this
24
+ The Data Control Samples n We consider the May10 events
only (no Razor Trigger)
n We classify events in the leptonic boxes by btag
n We further require mll > 60 GeV to enrich the di-lepton box in Zll events
OneLepton 0btag 1btag
70% ttbar
90% V+jets
SF TwoLeptons 0btag 1btag
80% ttbar
90% V+jets
OF TwoLeptons 0btag 1btag
95% ttbar
50% V+jets
✗
✔ ✔
✔ ✔ ✔
25
Control selections allow us to isolate specific backgrounds Parameters determined from these samples used as constraints in fits to ‘final’ fits on PromptReco (Razor triggered) samples
+ The Pieces Together n Identify a sideband in the R2 vs MR plane (box-dependent)
n In each box perform an extended and unbinned Maximum Likelihood Fit
n Once the shapes are fixed, they are used to predict the background in the signal regions (the tail)
n The extrapolation is done with toy MC samples, which allow inclusion of the error on parameters
26
R2
MR
Minimum R2 and MR set by trigger requirements
Fit Region
Signal Sensitive Region
+ Results – ELE-ELE Box 27
n Background predictions in signal sensitive region are propagated analytically from Fit Region
n Systematic uncertainties are ‘marginalized in’ through toy generations (using full covariance matrix from ML Fit)
p−
value
+ Results – ELE-ELE Box 28
n Projections from 2D fits
n Shows data entire R2/MR plane
n Here, error bands on ‘SM total’ are statistical only
n In some cases, background sub-components are ‘effective’ in that they do represent more than one background b/c for example:
We find that all ‘2nd’ (less steep) components are identical within fit precision
+ Results – MU-MU Box 29
p−
value
+ Results – MU-MU Box 30
+ Results – MU-ELE Box 31
p−
value
+ Results – MU-ELE Box 32
Background almost exclusively ttbar
+ Results – ELE Box 33
p−
value
+ Results – ELE Box 34
+ Results – MU Box 35
p−
value
+ Results – MU Box 36
+ Data/MC Agreement Summary 37
P-value0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
# / b
in (0
.05)
0
1
2
3
4
5
+ Model-Dependent Interpretation 38
n Explicit test of background only hypothesis vs. signal-plus-background hypothesis in signal-sensitive region, model-by-model, using likelihood ratio test-statistic
n Signal represented by 2-D binned templates (with corresponding systematic uncertainties – see next slide)
v.s.
+ Model-Dependent Interpretation 39
n Signal systematics are ‘marginalized in’ through systematic variations of shapes and normalizations of signal template in toy generation
n NOTE: Box-by-box correlations are taken into account in toy generation (relevant for multi-box combined limit)
+ Model-Dependent Interpretation 40
n Tag-and-probe DATA/MC correction factors (pT- and η- dependent) are derived from data and used to correct signal yield central values (background is completely data-driven and is insensitive to DATA/MC disagreement)
+ Model-Dependent Interpretation 41
n Tag-and-probe DATA/MC correction factors (pT- and η- dependent) are derived from data and used to correct signal yield central values (background is completely data-driven and is insensitive to DATA/MC disagreement)
+ Example Hypothesis Test 42
= log(Q) - All Boxesλ
-100 -50 0 50 100 150 200 250 300
a.u.
0
0.01
0.02
0.03
0.04
0.05
0.06
observedλ
b1-CL
s+bCL
-1 L dt = 800 pb∫CMS Preliminary -
= 240 GeV0M = 500 GeV1/2M = 10βtan
= 00A = +µsgn
= 2.6e-02sCL
= log(Q) - All Boxesλ
-100 -50 0 50 100 150 200 250 300a.
u.0
0.01
0.02
0.03
0.04
0.05
0.06
| b only)λP(
| s+b)λP(λMedian expected
68% prob expected band
-1 L dt = 800 pb∫CMS Preliminary -
= 240 GeV0M = 500 GeV1/2M = 10βtan
= 00A = +µsgn
+ Example Hypothesis Test 43
= log(Q) - MU-MU Boxλ
-200 -100 0 100 200 300 400
a.u.
0
0.02
0.04
0.06
0.08
0.1
0.12
= 1800 GeV0M = 240 GeV1/2M = 10βtan
= 00A = +µsgn
= log(Q) - HAD Boxλ
-200 -100 0 100 200 300 400
a.u.
0
0.01
0.02
0.03
0.04
0.05
0.06 = 1800 GeV0M
= 240 GeV1/2M = 10βtan
= 00A = +µsgn
= log(Q) - All Boxesλ
-200 -100 0 100 200 300 400
a.u.
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
| b only)λP(
| s+b)λP(
= 1800 GeV0M = 240 GeV1/2M = 10βtan
= 00A = +µsgn
+
+
Boxes are combined by adding test-statistic (log-likelihood ratio)
+ … other boxes
+ Final Model-dependent result 44
)2 (GeV/c0m0 500 1000 1500 2000
)2 (G
eV/c
1/2
m
200
400
600
800
(250)GeVq~
(250)GeVg~
(500)GeVq~
(500)GeVg~
(750)GeVq~
(750)GeVg~
(1000)GeVq~
(1000)GeVg~
(1250)GeVq~
(1250)GeVg~
(1500)GeVq~
(1500)GeVg~
-1Razor 800 pb
-1 = 7 TeV, Ldt = 800 pbs ∫CMS Preliminary
> 0µ = 0, 0
= 10, Aβtan
<0µ=5, βtan, q~, g~CDF <0µ=3, βtan, q~, g~D0
±
1χ∼LEP2 ±l~LEP2
= L
SPτ∼
95% C.L. Limits 1LepMedian Expected Limit
σ1±Expected Limit Observed LimitObserved Limit, HadObserved Limit, Lep
-1 = 7 TeV, Ldt = 800 pbs
+ Final Model-dependent result 45
)2 (GeV/c0m0 500 1000 1500 2000
)2 (G
eV/c
1/2
m
200
400
600
800
(250)GeVq~
(250)GeVg~
(500)GeVq~
(500)GeVg~
(750)GeVq~
(750)GeVg~
(1000)GeVq~
(1000)GeVg~
(1250)GeVq~
(1250)GeVg~
(1500)GeVq~
(1500)GeVg~
-1Razor 800 pb
-1 = 7 TeV, Ldt = 800 pbs ∫CMS Preliminary
> 0µ = 0, 0
= 10, Aβtan
<0µ=5, βtan, q~, g~CDF <0µ=3, βtan, q~, g~D0
±
1χ∼LEP2 ±l~LEP2
= L
SPτ∼
95% C.L. Limits 1LepMedian Exp. (Lep)
σ1±Exp. Limit (Lep) Observed Limit, Lep
-1 = 7 TeV, Ldt = 800 pbs
+ 46
BACK-UP SLIDES
+ Event Filters 47
+ Razor Triggers in the Menu
n Rates are taken from run 166033 and scaled to 1e33
n Rates for the pure razor triggers match estimates to 2 decimal places
n x-trigger object thresholds dictate design of final state ‘boxes’
48
+ Razor Single Leptons 49
n May10ReReco+”Golden” JSON (through 167746)
n Background shapes appear under control è extending analysis to ML Fit
n Here, background prediction from MC w/ PU event re-weighting and NLO x-sections
[GeV] (R > 0.5)RM400 600 800 1000 1200 1400
Even
ts / 4
0 G
eV
1
10
210
DATASM MCW+jetstop+XZ+jets
DiBosons
=7 TeVsCMS 2011 Preliminary
-1 L dt = 726 pb∫
MU Box WP80* PT 10 GeV Tight* PT 20 GeV ELE Box
Isolation Quantities corrected for PU
[GeV] (R > 0.5)RM400 600 800 1000 1200 1400
Even
ts / 4
0 G
eV
1
10
210
310DATASM MCW+jetsZ+jetstop+X
DiBosons
=7 TeVsCMS 2011 Preliminary
-1 L dt = 759 pb∫
PD E
lectronHad
+
HLT_E
le10_CaloId
L_TrkIdV
L_CaloIsoV
L _TrkIsoV
L_R025_M
R200_v*
PD M
uHad
+
HLT_M
u8_R025_M
R200_v*
+ Razor Di-Leptons PD
Doub
leElectron +
[GeV] (R > 0.5)RM200 400 600 800 1000
Even
ts / 4
0 G
eV
1
10
210 DATASM MCW+jetstop+XZ+jets
DiBosons
=7 TeVsCMS 2011 Preliminary
-1 L dt = 708 pb∫
WP80 / Tight*
PT 20 / 10 GeV
e μ ELE-MU Box
[GeV] (R > 0.5)RM100 200 300 400 500 600 700 800
Even
ts / 4
0 G
eV
1
10
210DATASM MCW+jetstop+XZ+jets
DiBosons
=7 TeVsCMS 2011 Preliminary
-1 L dt = 708 pb∫
HLT_E
le17_CaloId
L_CaloIsoV
L_ E
le8_CaloId
L_CaloIsoV
L_v*
MU
-MU
Box E
LE-E
LE Box
WP80/WP95*
PT 20/10 GeV
[GeV] (R > 0.5)RM200 400 600 800 1000 1200
Even
ts / 4
0 G
eV
1
10
210
DATASM MCW+jetstop+XZ+jets
DiBosons
=7 TeVsCMS 2011 Preliminary
-1 L dt = 708 pb∫
PT 15/10 GeV Tight/Tight*
Isolation quantities corrected for PU
PD D
oubleM
uon +
HLT_D
oubleM
u7_v* || H
LT_Mu13_M
u8_v*
+ The Second Component
A second component is needed in the kinematic region we did not fully probe last year (statistics) We use an ISR tagger based on MC-truth information:
- look for events with high-pT parton - generated by ME, not PYTHIA parton shower - in these events, the ttbar system recoils against the high-pT parton
The second component in each ttbar final state (hadronic, semileptonic, and dileptonic) is the same as for ISR-tagged events
51
+ Is the 2nd component universal?
n Apply fit to MC in each box n Cross-check for each MC sample
n Are the 2nd component parameters the same? n We might expect them to be due to ISR n 1st components expected to be different
Zjets MC in lepton boxes Shapes very compatible Fractions different but invariant under number of Btags
Shapes also invariant under number of Btags Can use B-veto to get clean sample of Zll. True for 1st component also
+ Results – ELE-ELE Box 54
p−
value
p−
value
+ Results – MU-MU Box 55
p−
value
+ Results – MU-ELE Box 56
p−
value
+ Results – ELE Box 57
p−
value
+ Results – MU Box 58
p−
value
+ Results – HAD Box 59
p−
value
+ Results – HAD Box 60
+ Results – HAD Box 61
p−
value
+ x-sections 62
Hadro-production x-sections
+ Signal Contamination 63
[GeV]0M500 1000 1500 2000
[GeV
]1/
2M
300
400
500
600
700
HA
D B
ox S
igna
l Con
tam
. (%
)-110
1
10
=7 TeVsCMS Simulation = +µ = 0 sgn 0
= 10 AβCMSSM tan
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
300
400
500
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700
HA
D B
ox S
igna
l Con
tam
. (%
)
-110
1
10
=7 TeVsCMS Simulation = +µ = -500 sgn 0
= 40 AβCMSSM tan
Plots show percent signal contamination in the fit region, calculated w.r.t. the actual number of observed events in data
tanβ = 10 tanβ = 40
Signal contamination in the fit region of the different boxes is small for models that are outside of the 35 pb-1 analysis exclusions – very small
around 750 pb-1 observed limit
HAD BOX HAD BOX
+ Signal Contamination 64
[GeV]0M500 1000 1500 2000
[GeV
]1/
2M
300
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700
ELE
Box
Sign
al C
onta
m. (
%)
-210
-110
1
=7 TeVsCMS Simulation = +µ = 0 sgn 0
= 10 AβCMSSM tan
[GeV]0M500 1000 1500 2000
[GeV
]1/
2M
300
400
500
600
700
MU
Box
Sig
nal C
onta
m. (
%)
-210
-110
1
=7 TeVsCMS Simulation = +µ = 0 sgn 0
= 10 AβCMSSM tan
Plots show percent signal contamination in the fit region, calculated w.r.t. the actual number of observed events in data
tanβ = 10
Signal contamination in the fit region of the different boxes is small for models that are outside of the 35 pb-1 analysis exclusions – very small
around 750 pb-1 observed limit
ELE BOX MU BOX
+ Signal Contamination 65
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
300
400
500
600
700
ELE
Box
Sign
al C
onta
m. (
%)
-210
-110
1
=7 TeVsCMS Simulation = +µ = -500 sgn 0
= 40 AβCMSSM tan
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
300
400
500
600
700
MU
Box
Sig
nal C
onta
m. (
%)
-110
1
10=7 TeVsCMS Simulation = +µ = -500 sgn
0 = 40 AβCMSSM tan
Signal contamination in the fit region of the different boxes is small for models that are outside of the 35 pb-1 analysis exclusions – very small
around 750 pb-1 observed limit
ELE BOX MU BOX tanβ = 40
Plots show percent signal contamination in the fit region, calculated w.r.t. the actual number of observed events in data
+ Signal Eff. tan β=10 66
[GeV]0M500 1000 1500 2000
[GeV
]1/
2M
100
200
300
400
500
600
700
Sign
al R
egio
n Ef
f. (%
)
0
5
10
15
20
25
30
=7 TeVsCMS Simulation = +µ = 0 sgn 0
= 10 AβCMSSM tan
[GeV]0M500 1000 1500 2000
[GeV
]1/
2M
100
200
300
400
500
600
700
HA
D B
ox S
igna
l Reg
ion
Eff.
(%)
0
5
10
15
20
25=7 TeVsCMS Simulation = +µ = 0 sgn
0 = 10 AβCMSSM tan
All Signal Regions (all boxes) HAD Box Signal Regions
Efficiency calculated w.r.t. inclusive SUSY cross-section
+ Signal Eff. tan β=10 67
MU Box Signal Regions
Efficiency calculated w.r.t. inclusive SUSY cross-section
[GeV]0M500 1000 1500 2000
[GeV
]1/
2M
100
200
300
400
500
600
700
ELE
Box
Sign
al R
egio
n Ef
f. (%
)
0
0.5
1
1.5
2
2.5
3
3.5=7 TeVsCMS Simulation = +µ = 0 sgn
0 = 10 AβCMSSM tan
[GeV]0M200 400 600 800100012001400160018002000
[GeV
]1/
2M
100
200
300
400
500
600
700
MU
Box
Sig
nal R
egio
n Ef
f. (%
)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5=7 TeVsCMS Simulation = +µ = 0 sgn
0 = 10 AβCMSSM tan
ELE Box Signal Regions
+ Signal Eff. tan β=40 68
All Signal Regions (all boxes) HAD Box Signal Regions
Efficiency calculated w.r.t. inclusive SUSY cross-section
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
100
200
300
400
500
600
700
Sign
al R
egio
n Ef
f. (%
)
0
5
10
15
20
25
30
=7 TeVsCMS Simulation = +µ = -500 sgn 0
= 40 AβCMSSM tan
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
100
200
300
400
500
600
700
HA
D B
ox S
igna
l Reg
ion
Eff.
(%)
02468101214161820
=7 TeVsCMS Simulation = +µ = -500 sgn 0
= 40 AβCMSSM tan
+ Signal Eff. tan β=40 69
Efficiency calculated w.r.t. inclusive SUSY cross-section
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
100
200
300
400
500
600
700
ELE
Box
Sign
al R
egio
n Ef
f. (%
)
0
0.5
1
1.5
2
2.5
3=7 TeVsCMS Simulation = +µ = -500 sgn
0 = 40 AβCMSSM tan
[GeV]0M400 600 800 100012001400160018002000
[GeV
]1/
2M
100
200
300
400
500
600
700
MU
Box
Sig
nal R
egio
n Ef
f. (%
)
0
0.5
1
1.5
2
2.5
3
3.5
4
=7 TeVsCMS Simulation = +µ = -500 sgn 0
= 40 AβCMSSM tan
MU Box Signal Regions ELE Box Signal Regions